一种基于粒子群优化的干扰点部署算法

doi:10.16182/j.issn1004731x.joss.18-0730

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (6): 1101-1110.doi: 10.16182/j.issn1004731x.joss.18-0730

一种基于粒子群优化的干扰点部署算法

方芳¹, 叶春明², 刘海波³

1. 安徽中医药大学,安徽合肥 230037;
2. 国防科技大学电子对抗学院,安徽合肥 230037;
3. 31108部队,江苏南京 210016

收稿日期:2018-10-31 修回日期:2019-01-04 出版日期:2019-06-08 发布日期:2019-12-12
作者简介:方芳(1982-),女,安徽安庆,硕士生,讲师,研究方向为数据分析,优化算法。
基金资助:
国防科技大学基金(zk170341),安徽省教研基金(kj2016A400),安徽中医药大学自然重点项目(2018zrzd12)

Jammer Placement Algorithm Based on Particle Swarm Optimization

Fang Fang¹, Ye Chunming², Liu Haibo³

1. Anhui University of Chinese Medicine, Hefei 230037, China;
2. School of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China;
3. 31108th Unit, Nanjing 210016, China

Received:2018-10-31 Revised:2019-01-04 Online:2019-06-08 Published:2019-12-12

摘要/Abstract

摘要： 针对现有的自组网干扰点部署算法未考虑目标移动和部署区域受限的情况,提出一种基于粒子群优化的干扰点部署算法。算法主要包括目标建模、攻击建模和优化计算3部分,采用离散事件仿真方法对目标网络节点移动特性和通信过程进行建模,利用简单粒子群算法实现最佳位置的选择。实验结果表明,该算法部署的干扰点对于不同移动方式节点的目标网络具有很好的干扰效果。当干扰区域受限,或是干扰距离、网络节点密度、节点速度、干扰点数量等条件变化时,算法性能均表现出良好的稳定性。

关键词: 无线自组网, 移动节点干扰, 干扰点部署, 粒子群优化

Abstract: A novel jammer placement algorithm based on particle swarm optimization is proposed to solve the problems of nodes’ mobility and restrictions on placement areas in ad-hoc network. There are three steps in this algorithm: network simulation, jamming simulation, and optimization. The algorithm simulates the network communication process and movement of nodes with discrete simulation method. Simple particle swarm optimization is applied to compute the exact coordinates of jammers. Experiment results show that this algorithm is good at jamming network nodes moving in different ways. The algorithm has stable performance with varying conditions such as different placement areas, jamming ranges, network density, speeds of nodes and numbers of jammers.

Key words: Ad-hoc network, jamming moving target, jammer placement, particle swarm optimization

中图分类号:

TP393

方芳, 叶春明, 刘海波. 一种基于粒子群优化的干扰点部署算法[J]. 系统仿真学报, 2019, 31(6): 1101-1110.

Fang Fang, Ye Chunming, Liu Haibo. Jammer Placement Algorithm Based on Particle Swarm Optimization[J]. Journal of System Simulation, 2019, 31(6): 1101-1110.

参考文献

[1] Chaubey N K.Security analysis of vehicular ad hoc networks (VANETs): a comprehensive study[J]. International Journal of Security and Its Applications (S1738-9976), 2016, 10(5): 261-274.
[2] Jameel F, Wyne S, Kaddoum G, et al.A Comprehensive Survey on Cooperative Relaying and Jamming Strategies for Physical Layer Security[J]. IEEE Communications Surveys & Tutorials (S1553-877X), 2018, 8(1): 1.
[3] Huo Y, Tian Y, Ma L, et al.Jamming Strategies for Physical Layer Security[J]. IEEE Wireless Communications (S1536-1284), 2018, 25(1): 148-153.
[4] Holme P, Kim B J, Yoon C N, et al.Attack Vulnerability of Complex Networks[J]. Physical review E (S2470-0045), 2002, 65(5): 56-71.
[5] Zhang D, Gogi S A, Broyles D S, et al.Modelling Wireless Challenges[C]. Proceedings of the 18^th annual international conference on Mobile computing and networking. Turkey: ACM, 2012, 1(1): 423-426.
[6] Zhang D, Sterbenz J P G. Analysis of Critical Node Attacks in Mobile Ad hoc Networks[C]. Reliable Networks Design and Modeling (ONDM), 6^th International Workshop on. Spain: IEEE, 2014: 171-178.
[7] 李文锋, 符修文. 无线传感器网络抗毁性[J]. 计算机学报, 2015, 38(3): 625-647.
Li Wenfeng, Fu Xiuwen.Suvey on Invulnerability of Wireless Sensor Networks[J]. Chinese Journal of Computer, 2015, 38(3): 625-647.
[8] Shankar A.Optimal Jammer Placement to Interdict Wireless Network Services [R]. Naval postgraduate school monterey ca, California, USA: dudley knox library, 2008.
[9] Feng J, Li X, Pasiliao E L, et al.Jammer Placement to Partition Wireless Network[C]. Globecom Workshops. USA: IEEE, 2014: 1487-1492.
[10] Feng J, Dixon W E, Shea J M.Fast Algorithms for Jammer Placement to Partition a Wireless Network[C]. IEEE International Conference on Communications. France: IEEE, 2017: 1-6.
[11] Gezici S, Bayram S, Kurt M N, et al.Optimal Jammer Placement in Wireless Localization Systems[J]. IEEE Transactions on Signal Processing (S1053-587X), 2016, 64(17): 4534-4549.
[12] Lall S, Alfa A S, Maharaj B T.Optimal Placement and Power Allocation for Jammers in Wireless Mesh Networks[C]. Vehicular Technology Conference (VTC Fall) 2015. USA: IEEE, 2015: 1-7.
[13] Liu J, Liu Z, Zeng Y, et al.Cooperative Jammer Placement for Physical Layer Security Enhancement[J]. IEEE Network (S0890-8044), 2016, 30(6): 56-61.
[14] Eisen J, Watson S, Willink T.Location Constrained Jamming: Surgical Link Removal Using Local Graph Partitioning[C]. 2018 International Conference on Military Communications and Information Systems (ICMCIS). Poland: IEEE, 2018: 1-7.
[15] 颛孙少帅, 杨俊安, 刘辉, 等. 未知拓扑无线自组网络多节点干扰决策算法[J]. 西安交通大学学报, 2018, 1(6): 91-97.
Zhuansun Shaoshuai, Yang Junan, Liu Hui, et al.An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks[J]. Journal of Xi’an JiaoTong University, 2018, 1(6): 91-97.
[16] 李尔玉, 龚建兴, 黄健, 等. 基于功能链的作战体系复杂网络节点重要性评价方法[J]. 指挥与控制学报, 2018, 4(1): 42-49.
Li Eryu, Gong Jianxing, Huang Jian, et al.Node Importance Analysis of Complex Networks for Combat System Based on Function Chain[J]. Journal of Command and Control, 2018, 4(1): 42-49.
[17] 孙成雨, 申卯兴, 盛浩, 等. 防空多传感器网络结构抗毁性优化设计[J]. 通信学报, 2017, 38(6): 118-126.
Sun Chengyu, Shen Maoxing, Sheng Hao, et al.Optimization design of structure invulnerability for air defense multiple sensor network[J]. Journal on Communications, 2017, 38(6): 118-126.
[18] Kennedy J.Particle swarm optimization[M]. Encyclopedia of Machine Learning. Boston MA: Springer, 2011: 760-766.
[19] 胡旺, 李志蜀. 一种更简化而高效的粒子群优化算法[J]. 软件学报, 2007, 18(4): 861-868.
Hu Wang, Li Zhishu.A Simpler and More Effective Particle Swarm Optimization Algorithm[J]. Journal of Software, 2007, 18(4): 861-868.

一种基于粒子群优化的干扰点部署算法

Jammer Placement Algorithm Based on Particle Swarm Optimization

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